Segmentation of ARX-models using sum-of-norms regularization

Segmentation of time-varying systems and signals into models whose parameters are piecewise constant in time is an important and well studied problem. It is here formulated as a least-squares problem with sumof-norms regularization over the state parameter jumps, a generalization of `1-regularization. A nice property of the suggested formulation is that it only has one tuning parameter, the regularization constant which is used to trade off fit and the number of segments.